Decoding and Selective Modulation of Human Memory During Awake/Sleep Cycles

清醒/睡眠周期期间人类记忆的解码和选择性调制

• 批准号: 10472000
• 负责人: ITZHAK FRIED
• 金额: $ 147.26万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10472000
• 关键词: Acoustic Stimulation; Alzheimer' s Disease; Animals; Area; Artificial Intelligence; Auditory; Behavior; Brain; Brain region; Cells; Clinical; Code; Cognitive; Coupling; Data; Development; Electric Stimulation; Electrodes; Electrophysiology (science); Episodic memory; Event; Face; Foundations; Future; General Population; Goals; Hippocampus (Brain); Human; Intervention; Knowledge; Life; Light; Measures; Medial; Memory; Memory Disorders; Memory impairment; Methodology; Modeling; Nature; Neurodegenerative Disorders; Neurons; Neurosciences; Participant; Patient Monitoring; Pattern; Performance; Personal Satisfaction; Persons; Physiologic pulse; Physiological; Physiology; Play; Prevalence; Probability; Process; Protocols documentation; Research; Resolution; Response to stimulus physiology; Retrieval; Rodent; Sample Size; Sampling; Scheme; Series; Site; Sleep; Sleep Architecture; Slow-Wave Sleep; Stimulus; System; Techniques; Technology; Temporal Lobe; Testing; Therapeutic; Therapeutic Intervention; Time; Tsunami; Visual; Wakefulness; aging population; awake; deep learning; deep learning algorithm; deep learning model; experience; improved; innovation; interdisciplinary approach; interdisciplinary collaboration; learning strategy; memory consolidation; memory process; memory recognition; memory retrieval; movie; multidimensional data; multidisciplinary; multimodality; nervous system disorder; neural patterning; neuromechanism; novel; novel therapeutics; predictive modeling; recruit; relating to nervous system; sleep behavior; tool; white matter

PROJECT SUMMARY/ABSTRACT Episodic memories integrate the content of human experience in space and time and constitute the core of one's identity. Memory formation involves processing, and constructing interpretations of the incoming information in our daily lives and is one of the first functions compromised in neurodegenerative diseases such as Alzheimer's Disease. With population aging, we face a “Cognitive Tsunami” of millions of people with memory disorders. Thus, understanding neural mechanisms of memory, and finding interventions that enhance these processes is a critical endeavor with the potential to improve the lives of countless people world-wide. Although it is established that memory is critical for cognitive well-being, and sleep is critical for memory consolidation, the underlying mechanisms in the human brain are poorly understood. Research on memory and sleep so far has suffered from a gap between non-invasive cognitive research in humans and detailed electrophysiological research in animals. Moreover, most human studies are dominated by stimulus response methodologies where the presented stimuli constitute limited, discretized, aspects of memory. This approach, albeit well-controlled, is far from the rich narrative of episodes we experience. Thus, to mechanistically probe human memory consolidation, it is imperative to (a) employ methodologies that incorporate the continuous and multimodal nature of experience; (b) identify relevant neural activation patterns and how they are transformed and reactivated during sleep; (c) establish means to causally modulate memory processes during sleep. Building upon our exploratory U01 project, this proposal seeks a breakthrough in our understanding by going beyond the state-of-the-art, and via the application of integrative and multidisciplinary approaches. It capitalizes on a highly unique opportunity to record and modulate neuronal activity of a large number of single neurons and neuronal assemblies in the human brain. In parallel, it exploits the high dimensionality of the data as an asset through the use of cutting-edge Deep Learning (DL) algorithms, which have emerged as promising analysis tools. Specifically, the project will investigate the presence, and decoding, of distributed neural patterns associated with memory for different aspects of experience during wakefulness and identify their reactivation during sleep. The proposal aims to selectively modulate memory via application of novel closed-loop stimulation in sleep in concert with the DL model predictions. We anticipate that this project is poised to shed light on the relationship between sleep and memory, and its modulation from a novel perspective. Such an ambitious goal can only be achieved with unrivaled combination of experience, access to a clinical setting, and interdisciplinary collaborations such as those proposed in this project. By combining the opportunity to stimulate and record neural activity with the computational power of artificial intelligence, this project aims to offer findings with far reaching implications for basic neuroscience and contribute to the development of novel therapies for human memory disorders.

项目摘要/摘要 情节记忆整合了人类在时空中经验的内容，并构成了一个人的核心 身份。内存形成涉及处理和构建对传入信息的解释 我们的日常生活，是在神经退行性疾病（例如阿尔茨海默氏症）中受到损害的最早功能之一 疾病。随着人口衰老，我们面临着数百万人记忆障碍患者的“认知海啸”。 这是了解记忆的神经机制，并找到增强这些过程的干预措施是 一项重要的努力，有可能改善全球无数人的生活。 尽管确定记忆对于认知健康至关重要，而睡眠对于记忆至关重要 合并，人脑中的潜在机制知之甚少。记忆和 到目前为止的睡眠已经遭受了人类无创认知研究之间的差距 动物的电生理研究。此外，大多数人类研究都以刺激反应为主 所提出的刺激构成记忆方面的方法。这种方法， 尽管控制得很好，但与我们经历的情节的丰富叙述相去甚远。那是为了证明 人体记忆合并，必须（a）纳入连续和的员工方法 经验的多模式性质； （b）确定相关的神经元激活模式及其如何转化 在睡眠期间重新激活； （c）确定在睡眠期间偶尔调节记忆过程的方法。 在我们的探索性U01项目的基础上，该提案在我们的理解中寻求突破 超越了最先进的方法，并通过应用综合和多学科方法的应用。 利用一个高度独特的机会来记录和调节大量的神经元活动 人脑中的单个神经元和神经元组件。同时，它利用高维度 通过使用尖端深度学习（DL）算法的数据作为资产的数据 作为承诺分析工具。具体而言，该项目将调查分布式的存在和解码 与记忆相关的神经模式，在清醒期间经验的各个方面相关并识别 它们在睡眠期间重新激活。该建议旨在通过应用新颖的选择性地调节记忆 与DL模型预测一致的睡眠中的闭环刺激。 我们预计这个项目被毒化为阐明睡眠与记忆之间的关系，以及 它从新颖的角度进行调制。这样的雄心勃勃的目标只能通过无与伦比的组合实现 经验，访问临床环境以及跨学科的合作（例如 项目。通过结合刺激和记录神经元活动的机会与 人工智能，该项目旨在提供对基本神经科学和 为人类记忆障碍的新疗法的发展做出贡献。